Results for “model-architecture”

Provides production-grade Laravel architecture patterns covering controllers, services, actions, Eloquent models, routing, queues, events, caching, and API resources.

Estructura aplicaciones Flutter en módulos independientes y reutilizables con flutter_modular, incluyendo diseño de paquetes, dependencias y configuración de Melos.

Analyzes code directories to create comprehensive C4 code-level documentation including function signatures, arguments, dependencies, and code structure.

Compare tradeoffs and recommend architectural patterns — dependency injection, service-oriented architecture, repository, domain events, circuit breaker, and anti-corruption layer. Use when choosing between design patterns, planning microservices boundaries, evaluating system design alternatives, or asking 'which pattern should I use' for a specific coupling or resilience problem.

Application architecture patterns and code organization — clean architecture, hexagonal architecture, feature-based structure, dependency inversion, module boundaries, SOLID principles at module scale. Activate on "clean architecture", "hexagonal architecture", "feature-based structure", "dependency inversion", "module boundaries", "architecture pattern", "folder structure", "fat controller", "circular dependency", "IoC container". NOT for microservice decomposition (use microservices-patterns) or database schema design (use database-design-patterns).

Applies SOLID principles, clean architecture layers, dependency injection, and patterns like CQRS and event sourcing to design scalable, maintainable systems.

Designs bounded contexts, creates domain models, uses aggregates, and implements value objects for complex domain logic.

Guides module boundaries, dependency direction, and domain structure. Do not use for local edits that leave architecture unchanged.

Enforces system design, project structure, dependency management, error handling, testing, and documentation standards at principal-engineer level.

Designs application architecture, selects patterns, structures code modules, and manages dependencies for new projects.

Design system architecture, evaluate microservices vs monolith, create architecture diagrams, analyze dependencies, choose databases, and make technical decisions with architecture decision records.

Maps domains, ownership, dependencies, entry points, tests, services, and risks before broad changes, review, migration, or handoff.

Organizes Flutter code into domain, data, and presentation layers with unidirectional dependencies, using BLoC, use cases, and repository patterns for testable, maintainable enterprise applications.

Designs distributed system architectures, decomposes monoliths into bounded-context services, recommends communication patterns, and produces service boundary diagrams and resilience strategies.

Organizes Flutter code by business features instead of technical layers, with self-contained folders for UI, logic, and data, plus a recommended project structure and dependencies.

Generate production-ready Dataverse SDK solutions with architecture recommendations, data models, and implementation code for common business use cases.

Provides living project knowledge for the OpenFoodJournal iOS app, including architecture, data models, service contracts, and conventions, to be consulted before any work on the project.

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.

Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.

Quarto agente do Time de Migração. Opera em duas fases. Fase 1: detecta a topologia do legado, sempre propõe uma topologia moderna alternativa e produz topology_decision.md (com pausa humana para aprovação). Fase 2: desenha as specs do sistema novo sob a topologia escolhida, produzindo target_architecture.md, target_domain_model.md, target_data_model.md e data_migration_plan.md, com rastreabilidade total para o legado. Ativação: /reversa-designer (geralmente invocado por /reversa-migrate).